Extra Virgin Research Articles

Screening tools combined with multivariate data analysis to predict or confirm virgin olive oil classification by the Panel test

AbstractA particular aspect of quality control of virgin olive oil (VOO) is the mandatory application, together with chemical and instrumental determinations, of a standardized and official method for sensory assessment. The latter, known as Panel test, is carried out by trained assessors and contributes to the classification of VOOs into three commercial categories (extra virgin, virgin, and lampante). One drawback of this method is related to the large number of samples to be analyzed, compared to the work capacity of a sensory panel, especially during the selection for purchase by companies that blend and market virgin oils and the quality control conducted by the authorities to verify the declared commercial category. For this reason, it is helpful to develop and validate robust and rapid screening methods, based on volatile fingerprints, to preclassify each sample into one of the three commercial categories. Considering the strict relation between volatile compounds and the main sensory attributes (fruity and defects), a gas‐chromatographic volatile fingerprint can be the right choice. In this paper, the comparison of two emerging techniques, namely, headspace‐gas chromatography‐ion mobility spectrometry (HS‐GC‐IMS) and flash‐gas chromatography (FGC), applied on a sample set of 49 VOOs, using calibrations previously built with a larger number of samples, is presented. The number of correctly classified samples, with respect to the commercial category determined by the Panel test, was satisfactory and comparable (92% for HS‐GC‐IMS, and 94% for FGC), confirming the effectiveness of both methods and the robustness of the predictive models.Practical Applications: The demand for rapid screening tools to reduce the number of samples to be assessed by the Panel test has increased in recent years. The validation of robust models and their joint adoption by companies that market VOOs as well as official control bodies could reduce nonconformities and increase the batches of VOO being controlled, thus better protecting the consumer. Therefore, it is desirable to have different tools available to analyze volatile compounds, together with the associated calibration models, along with detailed instructions for their application, to have different alternatives that suit the equipment of individual laboratories.

A comparative study of fluorescence and Raman spectroscopy for discrimination of virgin olive oil categories: Chemometric approaches and evaluation against other techniques

Spectroscopic techniques are presented as a reliable and economical alternative for the discrimination of olive oil quality categories. In this work, fluorescence and Raman spectroscopic techniques have been compared by evaluating different characteristics focused on sample preparation and analysis time, as well as classification percentages by chemometric models (PLS-DA) to classify olive oils into three categories: extra virgin (EVOO), virgin (VOO), and lampante (LOO). A statistical evaluation of the quality of the chemometric models was performed by comparing the parameters of sensitivity in terms of the true positive rate (%TPR), specificity (or true negative rate %TNR), and classification error in calibration, cross-validation and prediction. Fluorescence spectroscopy was the screening technique with the best overall accuracy in the ternary models for prediction (78.60%), with higher results than those obtained by Raman (66.03%), the latter being below the average error in the tasting panels. Regarding the classification obtained in binary models (EVOO/Non-EVOO and LOO/Non-LOO), fluorescence spectroscopy continued to present better results than Raman, with classifications of 92.5% and 88.5 % for both models, respectively (Raman obtained percentages of 70.7% and 66.6%). A comparison of the results obtained with confirmation techniques (headspace-gas chromatography-ion mobility spectrometry, HS-GC-IMS) and other spectroscopic techniques (NIR and MIR) studied in previous research was carried out, where it was observed that fluorescence spectroscopy obtained the closest classification percentage to the HS-GC-IMS technique (86.4%) in ternary models. On the other hand, the NIR technique obtained the highest classification percentage in binary models, with 92.6% for EVOO/Non-EVOO and 88.9% for LOO/Non-LOO compared to the value obtained using GC-IMS (95.4 and 86.4 %). Evaluating the rest of the parameters of interest for the olive industry, IR (both NIR and MIR) was the most interesting technique because it is a cheap, simple sensor with fast chemometric treatments and is environmentally-friendly. The results of this comparative study can be used to provide solutions and alternatives for applying new analytical strategies to support the work of tasting panels.

Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories.

Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and>80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and>90%).

Optimized variable selection and machine learning models for olive oil quality assessment using portable near infrared spectroscopy

Olive oil is a key component of the Mediterranean diet, rich in antioxidants and beneficial monounsaturated fatty acids. As a result, high-quality olive oil is in great demand, with its price varying depending on its quality. Traditional chemical tests for assessing olive oil quality are expensive and time-consuming. To address these limitations, this study explores the use of near infrared spectroscopy (NIRS) in predicting key quality parameters of olive oil, including acidity, K232, and K270. To this end, a set of 200 olive oil samples was collected from various agricultural regions of Morocco, covering all three quality categories (extra virgin, virgin, and ordinary virgin). The findings of this study have implications for reducing analysis time and costs associated with olive oil quality assessment. To predict olive oil quality parameters, chemical analysis was conducted in accordance with international standards, while the spectra were obtained using a portable NIR spectrometer. Partial least squares regression (PLSR) was employed along with various variable selection algorithms to establish the relationship between wavelengths and chemical data in order to accurately predict the quality parameters. Through this approach, the study aimed to enhance the efficiency and accuracy of olive oil quality assessment. The obtained results show that NIRS combined with machine learning accurately predicted the acidity using iPLS methods for variable selection, it generates a PLSR with coefficients of determination R2 = 0.94, root mean square error RMSE = 0.32 and ratios of standard error of performance to standard deviation RPD = 4.2 for the validation set. Also, the use of variable selection methods improves the quality of the prediction. For K232 and K270 the NIRS shows moderate prediction performance, it gave an R2 between 0.60 and 0.75. Generally, the results showed that it was possible to predict acidity K232, and K270 parameters with excellent to moderate accuracy for the two last parameters. Moreover, it was also possible to distinguish between different quality groups of olive oil using the principal component analysis PCA, and the use of variable selection helps to use the useful wavelength for the prediction olive oil using a portable NIR spectrometer.

QUALITY CHARACTERISTICS OF OILS FROM KERNELS OF VARIOUS APRICOT CULTIVARS OF THE ODESA REGION FOR THE DEVELOPMENT OF A CRAFT TECHNOLOGY

It has been studied whether the kernels (endocarps) of stones obtained from apricots grown in the Odessa region (the cultivars Shablovsky, Kyivsky Ananasny, Ananasny Tsyuryupinsky, and Krasnoshchoky, and their mixture, 25 % of each variety) are prospective raw materials for fat-and-oil and beauty products. The study provides a basis for a craft technology of producing oils from the kernels of stones of various apricot cultivars harvested in the region in 2020, 2021, and 2022. The most suitable cold pressing modes (extra virgin) have been chosen for kernels obtained from different cultivars. Crushed apricot kernels should be wet-treated and heat-treated at 40–50 °C for 15–20 min. During extraction by pressing, it is recommended that the crushed kernels (mash) should not be heated above 50—60 °C for 5–7 min. The effectiveness of pressing has been established: the kernels of stones of the cultivars Shablovsky, Kyivsky Ananasny, Ananasny Tsyuryupinsky, Krasnoshchoky (harvested in 2020, 2021, and 2022) and their mixture contained 33–47 % of oil. The thickness of an oilcake piece determines the effectiveness of pressing extraction. The thinner the oilcake piece, the more effective the pressing is: the time of resistance to stress, the pressing force, and the loading rate are significantly reduced, and the produce of oil increases. Thus, the parameters of the pressing extraction stage most effective for the kernels of all the apricot cultivars are: residual oil content in the oilcake 5.0–6.0 %, oil yield 94.0 %, oilcake thickness 33.0 mm, time of resistance to stress 3.0 min, pressing force 10.0 kN, loading rate 5.0 kN/cm. The proposed technological mode of producing oils allows retaining the original fatty acid composition of the raw material to a maximum extent. The fatty acid composition of oils produced from the kernels of apricot stones and their mixtures (harvested in 2020, 2021, and 2022) being determined, the results have shown that the difference in ω-6 PUFA was 2.9 % and that in ω‑9 MUFA was 7.1 %, depending on the cultivar.

Antioxidant Profile and Sensory Analysis in Olive Oils of Different Quality Grades

Antioxidant capacity and sensory analysis of olive oils of different quality grades (Extra virgin, Virgin, Ordinary and Lampante) were investigated to define their possible differences useful for quality discrimination. Total phenolic content discriminated the sample Lampante olive oil (LVOO) with values (0.95 mg GAE/g) significantly lower than the other oils (1.85, 1.80 and 1.98 for A, D and E samples, respectively). The principal component analysis (PCA) revealed that sensory attributes (“bitter” and “pungent”) and antioxidant capacity (expressed by FRAP and ABTS•+) are positively correlated with Extra-virgin olive oil (EVOO) and Virgin olive oil (VOO) categories, evidencing high values. In conclusion, based on the evaluated parameters, differences between the different olive oil categories were found. Still, they did not allow us to clearly separate the two categories of Extra-virgin olive oil (EVOO) and Virgin olive oil (VOO) oils.

Olive Yield and Physicochemical Properties of Olives and Oil in Response to Nutrient Application under Rainfed Conditions.

The effects of mineral fertilizers on the physicochemical properties of olives and oil under rainfed conditions is scarce. In this three-year study, the results of a nitrogen (N), phosphorus (P), potassium (K) and boron (B) fertilization trial carried out in a young rainfed olive grove and arranged as a nutrient omission trial are reported. The control consisted of the application of N, P, K and B (NPKB) and four other treatments corresponded to the removal of one of them (N0, P0, K0 and B0). Olive yield and several variables associated with the physicochemical properties of olives and oil were evaluated. The NPKB treatment increased olive yield compared to the treatment that did not receive N (N0). Although dependent on the climate conditions of the crop season, the NPKB treatment increased fruit weight and the pulp/pit ratio and its fruits tended to accumulate more oil than K0. However, the phenolics concentrations on fruits and oil tended to be lower. All olive oil samples were classified in the "extra virgin" category and all showed a decrease in its stability between 3 and 15 months of storage, regardless of treatment, especially in N0, P0 and B0 treatments. The results of the sensorial analysis indicate that all the oils fell into the medium fruitiness and greenly-fruity category. Only in P0 and B0 were defects detected, namely muddy sediment. Thus, this study seems to indicate the importance of N application, but also a balanced nutrient application and that further studies are needed, given the difficulty in finding clear trends in the response of measured variables to fertilizer treatments.

Novel time-domain NMR-based traits for rapid, label-free Olive oils profiling

Olive oil is one of the oldest and essential edible oils in the market. The classification of olive oils (e.g. extra virgin, virgin, refined) is often influenced by factors ranging from its complex inherent physiochemical properties (e.g. fatty acid profiles) to the undisclosed manufacturing processes. Therefore, olive oils have been the target of adulteration due to its profitable margin. In this work, we demonstrate that multi-parametric time-domain NMR relaxometry can be used to rapidly (in minutes) identify and classify olive oils in label-free and non-destructive manner. The subtle differences in molecular microenvironment of the olive oils induce substantial changes in the relaxation mechanism in the time-domain NMR regime. We demonstrated that the proposed NMR-relaxation based detection (AUC = 0.95) is far more sensitive and specific than the current gold-standards in the field i.e. near-infrared spectroscopy (AUC = 0.84) and Ultraviolet-visible spectroscopy (AUC = 0.73), respectively. We further show that, albeit the inherent complexity of olive plant natural phenotypic variations, the proposed NMR-relaxation based traits may be a viable mean (AUC = 0.71) in tracing the regions of origin for olive trees, in agreement with their geographical orientation.

Geographical discrimination of olive oils from Cv. ‘Galega Vulgar’

Olive oils from seven Portuguese regions were selected to study the effect of the geographical origin on the oils’ composition. Quality parameters, fatty acids, tocopherols, hydroxytyrosol and tyrosol derivatives, and oxidative stability were evaluated. All olive oils could be classified as extra virgin, and the geographical origin significantly affected the oils chemical composition. Principal component analysis further confirmed the significant impact of the geographical origin on the composition and, indirectly, on stability of the oils, showing that the evaluated parameters could be used as markers for geographical origin identification. Alternatively, Fourier transform infrared spectroscopy was applied, allowing to establish a linear discriminant model that correctly identified the geographical origin of the olive oils with a mean sensitivity of 99 ± 3 % (internal validation), confirming the impact of the oil origin on its characteristics. This finding allowed foreseeing the future application of the spectroscopy approach as a green, fast and non-invasive authentication tool.

Price risk connectedness in the principal olive oil markets of the EU

This work investigates price risk connectedness in the leading olive oil markets of the EU. To this end, it relies on a flexible methodology that allows modelling price linkages across multiple markets in the physical and the product quality space and weekly prices from Spain, Italy, and Greece. The main empirical results suggest: First, price risk connectedness under extreme positive shocks is stronger than under extreme negative ones. Second, physical space is a much more important determinant of price risk spillovers relative to quality differentials. Third, the higher quality of olive oil (extra virgin) is more likely to transmit price risk to the lower quality of olive oil (virgin). Fourth, Spain (the largest supplier) plays a central role in the flow of information within the network of olive oil markets.

Analyzing the Organoleptic Quality of Commercial Extra Virgin Olive Oils: IOC Recognized Panel Tests vs. Electronic Nose.

Virgin olive oil (VOO) classification into quality categories determines its labeling and market price. This procedure involves performing a series of chemical–physical analyses and, ultimately, a sensory analysis through the panel test. This work explores the analysis of VOOs quality with an electronic olfactory system (EOS) and examines its abilities using the panel test as a reference. To do this, six commercial olive oils labelled as extra virgin were analyzed with an EOS and classified by three panels recognized by the International Olive Council. The organoleptic analysis of the oils by the panels indicated that most of the oils in the study were in fact not extra virgin. Besides this, the classifications showed inconsistencies between panels, needing statistical treatment to be used as a reference for the EOS training. The analysis of the same oils by the EOS and their subsequent statistical analysis by PCA revealed a good correlation between the first principal component and the olive oil quality from the panels using average scores. It also showed a more consistent classification than the panels. Overall, the EOS proved to be a cheaper, faster, and highly reliable method as a complement to the panel test for the olive oil classification.

Purity of Olive Oil Commercially Available in Poland.

The aim of this study was to examine olive oils purchased in Poland for their compliance with label declarations and EEC criteria. Statistical analysis was used to compare the olive oils in terms of their content and composition of essential constituents and color parameters. Fifty olive oils (extra virgin, bioextra virgin, cold-pressed, refined, and pomace) from different countries (Spain, Italy, Greece, Portugal, Germany, France, Israel, and the European Union), were purchased commercially in Poland. The contents of triacylglycerols, sterols, and tocopherols, the fatty acid composition, and the color parameters were determined using chromatographic and spectrophotometric methods. Statistical methods were used to divide the olive oils into clusters. Our results show that the composition and color parameters of olive oils available commercially in Poland, excluding pomace olive oils, are similar. It can thus be concluded that, irrespective of the type of olive oil stated on the label, their quality is the same or very similar.

Evaluation of Pituitary-Testicular Axis and Lipid Profile Levels in Male Wistar Rats Administered Extra Virgin Olive Oil Versus Oleic Acid with a Normal Diet

Background: Dietary fats impact male reproductive functions. The effect of consumption of normal rat chow with the Mediterranean main fat, extra-virgin olive oil (EVOO), and its major component, oleic acid (OA) in non-metabolic disease states on reproductive hormonal functions is however unknown. Objectives: This study investigated the effect of EVOO and OA in normal diet feeding on reproductive hormones and lipid profiles. Methods: Eighteen male Wistar rats were divided into 3 groups and fed as follows: Group I (Control) fed the normal rat chow. Group II (extra-virgin olive oil: EVOO) fed the normal diet plus 1 ml/kg/day EVOO. Group III (oleic acid: OA) fed the normal diet plus 1 ml/kg/day OA as previously described. After 4 weeks of the experimental period, reproductive hormones, lipid profile, glucose, and total protein levels were assessed. Results: The serum luteinizing hormone (LH) level was significantly increased in both EVOO and OA groups, with no significant difference in serum testosterone (T) levels. The testicular T and serum oestradiol levels were significantly decreased in the EVOO groups but were significantly increased in the OA groups when compared to the control. There was a significant increase in the serum LDL-C in both EVOO and OA groups when compared to the control. The serum HDL-C levels were significantly decreased (p < 0.05) in both EVOO and OA groups when compared to control. Conclusion: The administration of EVOO and OA in normal diet feeding resulted in diminished serum testosterone levels and serum HDL-C levels.

A profound explanation of why eating green (wild) edible plants promote health and longevity

AbstractFrom 1970, research evidence has accumulated that the Mediterranean diet promotes health and longevity. Its main components include local (wild) green vegetables, citrus fruits, and olive oil (extra virgin). Since the 1990s, experimental research on phytochemicals to explain why plant food is healthy and promotes longevity has grown exponentially. Nowadays, molecular biology provides deep explanations for many experimentally found health‐promoting properties of plant species and their phytochemicals. The specialized approach is OK because it is the way research progresses. Mainly, nutritional researchers concentrate on a particular group of compounds such as flavonoids, phenolic compounds, carboxylic acids, fatty acids, and so forth. Science outside the research on nutrition deals with the same chemical compounds but which nutritional researchers generally do not follow. Plant biologists have found that all photosynthesizing plants share many compounds and ions. They are vital to plants. Some of the compounds and ions are also vital to humans. Plant biologists make a distinction between minerals, primary metabolites, and secondary metabolites. This distinction applies partly to humans. Plant minerals and primary metabolites often are essential to humans. Plant secondary metabolites are often not vital to humans but experimental research has shown that they promote health and longevity. Eating local wild edible plants (WEP) also promotes sustainability. WEPs are an ecosystem service. I have found 52 compounds and ions that all green edible plants share, promoting human health, well‐being, and longevity, and I present the evidence in this paper.

Effects of supplementing a healthy diet with pecan nuts or extra-virgin olive oil on inflammatory profile of patients with stable coronary artery disease: a randomised clinical trial.

The consumption of nuts and extra-virgin olive oil has been associated with suppression of inflammatory pathways that contribute to atherosclerosis, but its role on the modulation of the inflammatory profile in patients with established coronary artery disease (CAD) is unclear. The aim of this study was to evaluate the effects of adding pecan nuts or extra-virgin olive oil to a healthy diet on inflammatory markers in patients with stable CAD. In this randomised clinical trial, 204 patients were enrolled to three study groups: sixty seven to control group (CG: healthy diet), sixty eight to pecan nuts group (PNG: 30 g/d of pecans + healthy diet) and sixty nine to extra-virgin olive oil group (OOG: 30 ml/d of extra-virgin olive oil + healthy diet). High-sensitivity C-reactive protein (hs-CRP, in mg/l), fibrinogen (mg/dl), IL 2, 4, 6, 10 (pg/ml) and interferon-γ (IFN-γ, in pg/ml), IL-6/IL-10, IL-2/IL-4 and IFN-/γIL-4 ratios were evaluated at baseline and after the follow-up (12 weeks). As main results, after adjustment for sex, statin used and relative body weight variation, there were no differences between groups regarding inflammatory markers at the end of the study. IL-6 levels (primary outcome) were reduced in 12 weeks when compared with baseline in all study groups (CG: difference: -0·593 (se = 0·159) pg/dL; PNG: difference: -0·335 (se = 0·143) pg/dl; OOG: IL-6 difference: -0·325 (se = 0·143) pg/dl). In conclusion, there was no significant effect of including pecan nuts or extra virgin olive oil to a healthy diet on inflammatory markers in individuals with CAD.

Emerging trends in olive oil fraud and possible countermeasures

A review of most common types of fraud in the olive oil sector has been carried out. The work was supplemented by the results of an international on-line survey of EU and non-EU stakeholders in the olive oil sector. The review confirms that most common infringements (fraud or non-compliance) are the marketing of virgin olive oil as extra virgin, and blends of other vegetable oils (sunflower, corn, palm, rapeseed, etc.) with olive oil being marketing as olive oil. The on-line survey focused on current and future issues facing a range of stakeholders, e.g. exporters, importers, control laboratories. Of seemingly high priority to industry were emerging issues with regards to fraud arising from the addition of deodorized oil and from mixing with oil obtained by a second centrifugation of the olive paste (remolido). On the same line, a questionnaire, addressed to the EU Food Fraud Network National Contact Points, highlighted that the most frequent fraudulent practice is mixing with lower quality olive oils and that EU, non-EU and mix of EU and non-EU oils are the cases which need more control activities in relation to false designations of origin.

New insights into the Argan oil categories characterization: Chemical descriptors, FTIR fingerprints, and chemometric approaches

The characterization of Argan oils to classify them in three categories (‘Extra Virgin’, ‘Virgin’ and ‘Lower quality’) was evaluated. A total of 120 Moroccan Argan oils samples from the Taroudant Argan forest was investigated. The free acidity, peroxide value, spectrophotometric indices (K232 and K270), fatty acids, sterols, and tocopherol contents were assessed. The samples were also scanned by FTIR spectroscopy. The Principal Component Analysis (PCA) and four classification methods, Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modelling of Class Analogy (SIMCA), K-nearest Neighbors (KNN), and Support Vector Machines (SVM), were applied on both the chemical and spectral data. Besides the conventional chemical profiling, FTIR spectra were evaluated for their feasibility as a rapid non-invasive approach for classifying and predicting the oil quality categories.The most important variables for differentiating the oil categories were identified as K232, peroxide value, ɣ-tocopherol, δ-tocopherol, acidity, stigma-8-22-dien-3β-ol, stearic acid (C18:0) and linoleic acid (C18:2) and could be used as quality indicators. Eight chemical descriptors or key features from the FTIR spectra (selected by interval-PLS) could also be established as indicators of quality and freshness of Argan oils.

Chemical and volatile characteristics of olive oils extracted from four varieties grown in southwest of China

The present study was conducted to determine the quality parameters, fatty acid profile, minor compounds (pigments, tocopherols, phenolic compounds, squalene and total sterols) and volatile compounds of olive oils from four common olive cultivars (cv. ‘Koroneiki’, ‘Coratina’, ‘Frantoio’ and ‘Arbequina’) planted in China. The effect of maturation stage on the characteristics of the oils was also evaluated. All samples were classified as extra virgin according to the standards established by IOC. Statistically significant differences (p < 0.05) were observed in the most analytical indicators of the oils among the cultivar and ripening. Coratina oils contained the highest contents of carotenoids, chlorophylls, tocopherols, phenolic compounds and high level of volatiles, demonstrating their excellent nutritional qualities and pleasant flavors. Whereas, Koroneiki oils contained the highest contents of oleic acid and squalene. Further, high levels of total sterols were found in Frantoio and Arbequina oils. Phenolic compounds and volatiles decreased with increase of ripe degree, which indicated the oils from green olive fruits possess better quality and flavor.

Statistical Analysis of the Physicochemical Parameters of Olive (Olea europaea L.) Ecotype Oils in the Oasis Region of Degache (Tunisia)

Summary A total of seventeen olive ecotypes were identified in the oasis region of Degache (Tozeur, Tunisia). The following physicochemical parameters of their oils were studied: acidity, refraction index, polyphenol, chlorophyll and carotene contents, oleic, palmitic and linoleic acid levels and the UV absorbance (K232 and K270). The acidity, K232 and K270 parameter values were used to classify most of the olive oils considered as ‘extra virgin’. The highest carotene, chlorophyll and polyphenol contents of the olive oils examined were found to be 13.60 ppm, 9.10 ppm and 796 ppm, respectively. The oleic acid level of the olive oils considered ranged between 47% and 73%, whereas the palimtic acid level ranged between 11.8% and 23%, and the linoleic acid level ranged between 8.8% and 22.5%. Therefore, the fatty acid composition of the most olive oils considered was in keeping with the IOC norms. The Pearson correlation coefficients obtained were found to be negatively significant for the oleic, palmitic and linoleic acid levels recorded. The cluster and principal component analyses performed revealed the importance of fatty acids and polyphenol contents in the clustering of olive ecotypes. The selection of olive ecotypes according to oil quality is possible, requiring further studies focusing especially on their agronomic characters.

Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics.

The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors’ workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME–GC–MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel’s workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples.